import gene_exp_10x
df_ini = gene_exp_10x.load_gene_exp_to_df('../download/atlas/atlas_data_5k-sum/')
df_ini.shape
gene_sum = df_ini.sum(axis=1)
gene_sum.shape
from clustergrammer2 import net
def umi_norm(df):
# umi norm
barcode_umi_sum = df.sum()
df_umi = df.div(barcode_umi_sum)
return df_umi
df_small = umi_norm(df_ini.iloc[:,:5000])
df_small.shape
net.load_df(df_small)
net.filter_N_top(inst_rc='row', N_top=500, rank_type='var')
net.normalize(axis='row', norm_type='zscore')
net.clip(-5,5)
net.load_df(net.export_df().round(2))
net.widget()